Lavanya Laguduva

Developed and maintained comprehensive integration testing frameworks for AI-powered Open Intelligence APIs that use Large Language Models (LLMs) and embeddings to extract and match marketing entities from campaign briefs with graph database relationships.

• Developed integration test suites for AI-powered API that uses Google Gemini LLM to extract structured entities (products, brands, interests, locations) from marketing campaign brief PDFs
• Validated vector similarity search algorithms using PostgreSQL pgvector and Vertex AI embeddings to match extracted keywords with stored entities, implementing configurable similarity thresholds
• Validated complex Neo4j Cypher queries for traversing graph relationships, testing audience discovery based on interest/brand/product combinations
• Implemented performance testing for large result sets, ensuring query performance and result consistency

Focusing on expanding my expertise in AI concepts, frameworks, and tools – including Deep Learning, LLMs, Agents, and MCP. Built small, practical projects that apply these technologies to areas such as automating test strategy, generating test scenarios, test scripts, and performing intelligent code reviews.

• Architected a high-performance automation framework using Python, Playwright, and Pytest-BDD, significantly enhancing scalability and maintainability.
• Delivered high-quality API, UI, SIT, and E2E testing across multiple complex product areas.
• Built a comprehensive validation suite for event-driven architectures, ensuring message queue reliability through AWS SDK–based testing, API validations, and SQL checks.
• Introduced PACT contract testing, improving integration reliability by 40% across microservices.
• Validated end-to-end ETL pipelines and data transformations using AWS Athena, Glue, and Crawlers, ensuring accurate downstream representation in Tableau dashboards.
• Integrated CI/CD automation with GitLab, enabling continuous validation, faster feedback cycles, and robust regression coverage.
• Tested a wide range of AWS services including S3, message queues, event buses, Athena, Glue, and Tableau, and incorporated them into the automation framework using the AWS SDK.
• Automated performance testing using Python and JMeter to identify, analyse, and resolve system bottlenecks.

• Led the Central Integration Team, driving platform-wide quality engineering, system validation, and cross-service integration reliability.
• Built and maintained a Java + Gradle automation framework supporting more than 6,000 automated tests, significantly improving regression coverage.
• Developed a python based framework using PyTest and Integrated Kubernetes APIs to validate deployments, environments, and infrastructure behaviours as part of the automated pipeline.
• Delivered testing for a large-scale data migration and transformation programme, validating millions of records, numerous business rules, reconciliation checks, and data replication accuracy through automation and SQL checks.
• Optimised CI/CD pipelines by implementing automated testing triggers, enabling early defect detection and faster release cycles.
• Implemented security and accessibility testing across all components using OWASP ZAP and Axe, strengthening platform resilience and compliance.
• Validated multiple AWS services—including S3, Secrets Manager, Kafka, message queues, and Postgres SQL—and integrated them into the automation framework via AWS SDK for consistent, scalable testing.
• Mentored cross-functional teams on quality-as-code, automation best practices, and platform engineering principles, elevating overall engineering maturity.
• Achieved a 90% reduction in manual testing effort through comprehensive automation strategies and reusable test components.

• Led testing initiatives for a large-scale design system platform and reusable component library, ensuring consistent UX and engineering alignment across products.
• Developed a comprehensive JavaScript/TypeScript automation framework with full CI/CD integration on Azure DevOps, supporting rapid and reliable delivery cycles.
• Implemented automated microservices and GraphQL testing for a Mortgage product, ensuring end-to-end functional accuracy and integration stability.
• Established cross-platform and cross-browser compatibility testing using BrowserStack, enabling validation across a wide range of devices and environments.
• Created an accessibility validation framework to ensure compliance with A11Y standards and improve overall user experience.
• Drove standardisation initiatives across engineering teams, reducing development rework by 60% and improving delivery consistency.
• Delivered a cohesive, high-quality design system platform, accelerating development productivity across teams.
• Built reusable testing components to enhance scalability, maintainability, and modularisation of quality tooling.

• Built and maintained Python + Selenium automation frameworks for web applications that processed credit scores and recommended loan, credit card, and mortgage products.
• Tested and automated microservices and REST APIs using Postman/Newman, incorporating mock services based on Swagger/OpenAPI specifications.
• Validated data migrations and data integrity across PostgresSQL, AWS storage buckets, and DynamoDB, ensuring accurate and consistent transformation.
• Integrated automated tests into Jenkins CI pipelines, enabling continuous and reliable quality checks.
• Deployed services in a global CI/CD environment using Nomad, Consul, Vault, Jenkins, Kubernetes, Docker, and AWS, supporting scalable and resilient delivery.
• Demonstrated strong T-shaped skills by contributing to design and development activities when required.
• Collaborated closely with developers and product owners to define acceptance criteria, execute acceptance testing, and automate end-to-end workflows.

Worked on a large-scale data processing programme involving log files generated by cash tills across 4,000+ stores. The solution applied 300+ pages of complex business rules to transform and validate raw till logs before storing the processed output in GCP BigQuery for analytics and reporting. The project required deep understanding of intricate business logic, high data volumes, data quality validation, and cloud-based processing pipelines.

• Built a test team from scratch, scaling it to 3 onsite and 8 offshore engineers, and established the complete QA process for multiple agile teams.
• Designed and developed an automation framework using Java, Cucumber, and WebDriver, enabling scalable and maintainable regression coverage.
• Created a specialised cloud automation framework using Google APIs to validate data processing and integrations within GCP services.
• Set up a Jenkins-based Continuous Integration ecosystem to automate execution of test suites and improve feedback cycles.
• Provided strong technical leadership, mentoring team members and ensuring high-quality delivery across all testing phases.
• Performed end-to-end QA activities across multiple projects, consistently delivering on schedule with high accuracy and quality assurance standards.